Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data.

Journal: BMJ open

PMID: 31784446

Abstract

OBJECTIVES: We aimed to test whether or not adding (1) nutrition predictor variables and/or (2) using machine learning models improves cardiovascular death prediction versus standard Cox models without nutrition predictor variables.

Authors

Joseph Rigdon

Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, CA.
Sanjay Basu

Center for Primary Care and Outcomes Research, Center for Population Health Sciences, Departments of Medicine and Health Research and Policy, Stanford University, Palo Alto, CA basus@stanford.edu.

Keywords

Aged Algorithms Cardiovascular Diseases Diet Records Female Humans Machine Learning Male Middle Aged Nutrition Surveys Proportional Hazards Models Retrospective Studies Risk Assessment United States

External Resources

View on PubMed Access via DOI PubMed (31784446)

Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data.

Abstract

Authors

Keywords

External Resources

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